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The Great Leveling
There’s a phrase making the rounds in tech circles: “vibe coding.” Coined by Andrej Karpathy in early 2025, it describes a new approach to software development where you describe what you want in plain language and AI handles the rest. You don’t read the code, you don’t debug line by line—you just iterate through conversation until something works.
It sounds absurd to anyone who came up writing every semicolon by hand. And yet, by mid-2025, a quarter of Y Combinator’s winter batch had codebases that were 95% AI-generated. Collins English Dictionary named “vibe coding” their Word of the Year for 2025.
Whether you love it or hate it, the trend reveals something important: code itself is becoming commoditized. When anyone can prompt an AI to generate a functional prototype in an afternoon, the implementation details matter less than they used to. What matters more is what you’re building and why.
The Commoditization Playbook
This pattern has played out before. In the 1990s, Microsoft commoditized PC hardware by licensing Windows to every manufacturer willing to play along. Hardware prices collapsed while Microsoft’s operating system became the center of gravity. The strategic insight was simple: devalue the parts of the stack you don’t control, and capture value where you do.
AI is running a similar playbook on code itself. When powerful language models are available through a single API call, and open-source alternatives like DeepSeek demonstrate that you don’t need billions in compute to produce competitive results, the underlying code becomes a standardized, interchangeable piece of the puzzle.
Research from McKinsey’s 2025 State of AI report found that while 92% of companies plan to increase their AI investments, only 1% consider themselves mature in deploying AI across their workflows. The gap isn’t technical—it’s strategic. Companies are investing in AI without clarity about what they’re actually trying to accomplish.
Where Value Moves
If code is the new commodity, where does value migrate? The emerging consensus points in two directions: up the stack toward applications and ideas, and down the stack toward proprietary data and domain expertise.
As W. Chan Kim and Renée Mauborgne argue in Harvard Business Review, companies that treat AI as the answer—rather than a tool in service of strategy—put the cart before the horse. Success comes from starting with a clear vision of buyer value, then asking how AI might help deliver it.
The implications are significant. A customer service chatbot is a commodity. An AI system that triages support tickets, cross-references customer history, queries internal knowledge bases, and drafts contextual responses for human approval—that’s a competitive advantage. The difference isn’t the model. It’s the integration, the workflow design, the understanding of what the business actually needs.
For small businesses especially, this reframing matters. You don’t need to compete with OpenAI on model quality. You need to compete on understanding your customers, your domain, and your unique problems better than anyone else. The AI is just the implementation detail.
The Developer’s Evolving Role
So what happens to developers? The obituaries for software engineering are premature, but the job is changing.
The vibe coding experiments have been instructive here. As one observer noted, the approach “removed all of the actual implementation difficulty, but I’m still left with a lot of the conceptual difficulty of deciding what the behavior of the software should be.” Another senior engineer described the shift as “spending 80% of my time on architecture and 20% on implementation—exactly the opposite of five years ago.”
The transition from “vibe coding” to what’s now being called “context engineering” captures this evolution. Early vibe coding suffered from users demanding more than models could reliably deliver. Prompts grew larger, outputs became less predictable. The solution has been to get serious about managing context—curating instructions, structuring inputs, understanding what these systems can and cannot handle.
This is skilled work. It requires abstract knowledge of how software works, even if you’re not writing every line. It requires taste about what constitutes good design. It requires the ability to recognize when AI-generated code is subtly wrong in ways that won’t surface until production.
The developers who thrive will be the ones who can articulate technical guidance precisely, who understand system architecture deeply enough to direct AI tools effectively, and who bring domain expertise that models don’t have access to. Counterintuitively, AI-assisted development may require deeper computer science fundamentals, not shallower ones.
Ideas as the Scarce Resource
There’s a common assumption that AI will eliminate the need for technical expertise. The reality seems more nuanced. What AI eliminates is tedium. What it amplifies is the importance of good ideas.
Consider what vibe coding actually involves at its best: extended back-and-forth conversations about algorithms, integration of research findings, careful specification of edge cases. The AI is happy to follow your thinking, but it won’t generate that thinking spontaneously. The creative direction, the problem framing, the insight about what would actually be useful—that’s still on you.
For businesses, this means the competitive advantage shifts toward people who understand problems worth solving and have clear visions for how to solve them. Proprietary data matters. Domain expertise matters. Customer understanding matters. The ability to see an opportunity that others have missed and articulate it clearly enough for implementation—that’s what gets harder to replace.
Practical Implications
If you’re running a small business trying to figure out AI, the takeaway isn’t that you need to learn to code. It’s that you need to get clearer about what your business actually does, what problems your customers face, and where automation could genuinely help.
The businesses seeing real returns from AI aren’t the ones chasing the latest model releases. They’re the ones asking better questions: What workflows are genuinely bottlenecked by human attention? What data do we have that’s unique to our operation? What do we understand about our domain that competitors don’t?
If you’re not sure where to start, that’s where outside perspective helps. Magic Ingredient works with small businesses to answer exactly these questions—mapping your operations to find where AI fits naturally, not where it’s being forced.
Code is becoming a means to an end. The end is still valuable products, useful services, and real solutions to real problems. The winners in this new landscape will be the ones with the clearest view of what those look like.
Innovation and ideas have always mattered. Now they may be the primary thing that matters. The question for every business is whether you’re investing in developing them—or still treating AI as a magic solution rather than a tool in service of a larger vision.
If you’d like help figuring out where your ideas and AI intersect, we’d love to talk.